GIS Work Sample

GIS

Selected Works (2015 - 2019)

Wenhao Wu LEED Green Associate Master of City Planning, Smart City + Urban Design Urban Design Certificate (UPenn) B.Econ & B.S. Applied Maths (Renmin University)

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Mapping Colombian Caribbean Coast Cartagena, Colombia Urban Design Research Studio | PennDesign Instructor: David Gouverneur & Maria Villalobos

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Driven by the growing trades of natural resources and manufactured products, the ports in Colombia have seen increasing expansion in recent years, especially in coastal cities such as Cartagena. Connecting to them are established and new logistics routes that support the nation's fast developing economy. Focusing on the Caribbean Coast, by examining and mapping these situations on national, regional and city level, we can gain better and quick understanding on the evolving infrastructure system and its relationship to urbanization.

Responsibilities: Research, data collection, GIS database, cartographic design, diagramming and map production, etc.

Softwares: ArcGIS, Excel, Illustrator, Photoshop, InDesign

Regional Logistics and Energy Infrastructure Map

Web GIS National Transportation Routes Map for Exports / Imports

Ports in the City of Cartagene

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Comprehensive Planning, City of Mobile, AL Mobile, Alabama Urban Planning Internship | WRT

Inspired by the landscape architect and regional planner Ian McHarg, I developed a systematic GIS model to analyze existing condition for the City of Mobile, AL. Covering aspects of natural resources, transportation, population, employment, and socioeconomic conditions, we used GIS to assess and identify high potential growth areas for the city's long range comprehensive plan. A series of maps were created to be used in meetings with local stakeholders and help mapping out the city's organic future development.

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Responsibilities: data collection and cleaning, quantitative analysis, GIS modeling, database maintenance, map production, etc.

Softwares: ArcGIS, Excel, Illustrator, InDesign

Web GIS

Map "Cake" - Overlay Vector Analysis

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Holmesburg 2036 Neighborhood Plan Holmesburg, Philadelphia, PA Planning Workshop | PennDesign Instructor: Karen Thompson

Holmesburg is a unique neighborhood with a variety of elements located in northeast Philadelphia, Pennsylvania. In the first stage study, using both qualitative and quantitative methods, I studied the neighborhood’s existing conditions in aspects including open space, transportation, and land use.

Based on this study, the planning workshop worked with the community association to propose a series of neighborhood improvement and revitalization actions.

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Open Space and Bike Paths

Key Edges

Transit Accessibility

Responsibilities: Research, GIS data collection, existing condition analysis, mapping, report design, etc.

Softwares: ArcGIS, Illustrator, Photoshop, InDesign, Excel

211 acres

Web GIS

211 acres of park space; 484.2 sqft per person, larger than Philly's 302.3 sqft.

A planned Delaware River Greenway along the waterfront.

I-95

highway

I-95, State Road, and the Regional Rail provides easy access to the neighborhood, while segregating residents from the waterfront.

Frankford Avenue - existing condition

25

minutes 25 mins distance from Holmesburg Junction Station to Center City.

8.3% cheaper

Median House Value is $133,315, 8.3% lower than Philadelphia as a whole.

1.12 miles

1.12-mile long commercial corridor Frankford Avenue; Partially within a Business Improvement District.

Frankford Avenue - proposed

Manchester, United Kingdom Modeling Geographical Objects | PennDesign

This is a GIS project that utilizes a sequence of spatial analytical tools and organizes them into a computational model through model builder. The four tasks on the right explains the logical steps from identifying demands in terms of transportation driven attractiveness, to locating supply factors such as buildable zones and accessibility, and finally matching these factors to number of travelers.

TASK 1 Select the potential buildable zones for intensive construction of high-speed rail lines.

TASK 3 Find the buildings that are the best accessible to railway stations in the worst accessible LEP.

Web GIS

TASK 4 Find the railway station carrying the highest passenger volume in a low property value area.

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Responsibilities: thematic research, GIS data collection, GIS model building and execution, map and report production, etc.

Softwares: ArcGIS, Illustrator, Photoshop, InDesign, Excel

TASK 2 Find the Local Enterprise Partnerships (LEP) with the strongest correlation between proximity to named places and total coverage of roads.

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Model-Builder for High Speed Rail Location

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Drone Distribution Center Siting Analysis Franklin County, CT Modelilng Geographical Space | PennDesign Instructor: Prof. Dana Tomlin

With development of e-commerce and drone technology, the logistics industry is going to see a transition from using human driver and trucks to distributing goods by drones. The question of siting drone distribution center become a natural first step. As a proof-of-concept, a variety of geographical attributes were examined and incorporated into a raster based GIS model that calculates the overall suitability for distribution center sites and ranks them accordingly.

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Responsibilities: GIS data collection, modeling, map and report production, etc.

Softwares: ArcGIS (raster calculator, spatial analyst, model builer)

Preliminary Data Processing Model

Composite Raster Scoring Model

Composite Site Suitability Score Map

Reclassified Habitat

Web GIS

Land Use Value

Habitat Score

Accessibility Assessment

Puddle Centers

Reclassified Relative Population

Philadelphia Zoo, Philadelphia, PA Modeling Geographical Space | PennDesign

Cost Distance Percentage Change for the Three Food Trucks

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Impact of Flood on Food Truck Business

This project tries to analyze the accessibility difference between normal and rainy weathers for certain food truck businesses. Spatial algorithms of flow accumulation are used to simulate the "difficulty" of crossing for a potential food truck customer, including avoiding flooded areas, nonwalkable structrues, and restricted zones. 3D visualizations were created to represent the overall change of accessibility.

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Responsibilities: GIS data collection, GIS model building and execution, map and report production, etc.

Softwares: ArcMap (flow accumulation, cost distance, raster calculator, etc.), ArcScence

North Truck

South Truck

Web GIS

Middle Truck

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Identifying Urbanization through Cloudbased Raster Computing Pearl River Delta, China Google Earth Engine This project showcases the application of Google Earth Engine in detecting large urban conurbations and characterising industrial areas.

Responsibilities: data search and compilation, script development, visualization, etc.

Softwares: Earth Engine, javascript, Illustrator

Guangzhou 1984

Guangzhou 2015

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Global Nightlight

Major Urban Agglomeration

Night Light Trending Analysis

Night Light Trend, City Level, Changsha

New Urban Expansion Searching Around

Urban Area at Year 1 Google Earth Engine Script (real-time raster computation on the cloud)

Web GIS

Urban

Guangzhou Urban Area at Year 0

Rural

Intensity of Change

Foshan

Industrial Concentration Area (Red)

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Cloud-based Geospatial Data Visualization Various Locations Academic Projects | PennDesign

This page presents a number of large scale geospatial data visualization realized through a combination of tools from QGIS, ArcGIS, Python, and Google Earth Engine.

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The images on the upper half are predicted visitation rates, represented by density of flickr photos throughout the City of Boston and the City of San Francisco, based on a regression model built using open-source flickr historic data from 2008 2018. The InVEST tourism analysis tool was used for creating the visualization.

Images on the bottom half are large scale, real-time snapshots of computation results using JavaScript codes on Google Earth Engine, covering the topics of property values, access to urban amenities, population density, urban development, etc. in several different contexts.

Responsibilities: topic definition, methodology research, geospatial analysis, data collection and database management, code development, data visualization, etc.

Softwares: QGIS, ArcMap, Google Earth Engine, JavaScript, InVEST tourism prediction tool.

Web GIS

Population Density in Northern India and Pakistan

Population Density in Kunming, Yunnan Province, China Using Flickr data concentration to predict tourism activities

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Predicting Parcel Development Potential with Machine Learning San Francisco, CA Machine Learning Research | SOM City Design Practice Trying to predict where the next wave of new developments are likely to happen, we developed a Machine Learning model to "learn from the past". Various factors are incorporated into the model, including property based information, sociodemographic info, development pipeline data, and geospatial measurement data. The prediction results show interesting findings regarding potential parcels with high susceptibility.

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Responsibilities: All graphics, other than noted, are produced by myself.

Softwares: ArcGIS, QGIS, Excel, Orange Programming: Javascript, R, HTML, CSS, Mapbox GL JS Platforms: Mapbox, leaflet, open street map

1. The model used development pipeline data to construct the training.

Displaying population density in 3D on the fly

Geospatial feature values are created based on proximity to a series of urban "attractions" - infrastructures or amenities, including city and community parks, public transit, bicycle facilities, etc., and to "constraints", such as seismic hazard zones. The process is summarized in the diagram above. These geospatial features, together with the intrinsic value of the parcel info, such as land use, size and year of the development, and also the development pipeline records, form the backbone data that our Machine Learning model is constructed with.

2. Visualizing seismic hazard (liquefaction) zones and predicted susceptible parcels.

Web GIS Scan to visit the web app

3. Inspecting development related information by clicking on individual parcel.

Development Explorer

AdapKIT Climate Resilience Monitor

An integrated mapping platform with analytics and visualization functionalities

Urban heat island analysis&visualization platform. Selected as one of four finalists in European Commission's H2020 Innovation Competition.

Landing page with login information

Selecting countries from the covering region

Mapping page with data uploading option

Mapping various flood related information

Mapping thematic information

Launching the analytics component

Displaying building roof tops and vegetated surfaces

Flood risk areas and street network

Analyzing local concentration of challenges

Getting PDF report about individual development challenges

Intensity of Urban Heat Island (UHI) at neighborhood scale

Flood impact on buildings

Paraguay and Turino, Italy Web Mapping Application Development | GeoAdaptive LLC

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Web Mapping Application Development

Location matters. The way we collect, analyze and represent geographic information is becoming increasingly interactive, web platform-as-a-service is an emerging trend to go.

An important interest for me is geospatial analysis and web product development of mapping-based applications. It is a valuable process to relate objects and information in space, and analyze and reveal spatial patterns that have impacts on planning related decision-making.

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Responsibilities: UI /UX design, front-end & back-end programming, data processing and analysis, graphic production, etc.

Softwares: ArcGIS, QGIS, Excel, Atom Programming: Javascript, HTML, CSS Platforms: Github, leaflet, open street map, bootstrap

Web GIS